Delete app.py

app.py

@@ -1,241 +0,0 @@
-import os
-import json
-import threading
-import re
-from typing import Dict, Any
-
-import gradio as gr
-from fastapi import FastAPI, Depends, HTTPException
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
-import uvicorn
-
-from transformers import pipeline
-
-# ===== LangChain imports (v0.3.x + community modules) =====
-from langchain_community.llms import HuggingFacePipeline
-from langchain_community.utilities import SerpAPIWrapper
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
-
-from langchain.memory import ConversationBufferMemory
-from langchain.agents import initialize_agent, Tool
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.docstore.document import Document
-from langchain.tools.python.tool import PythonAstREPLTool  # ✅ Updated Python REPL
-
-# ===========================================
-# ENVIRONMENT VARIABLES
-# ===========================================
-HF_TOKEN = os.getenv("HF_TOKEN")
-SERPAPI_KEY = os.getenv("SERPAPI_API_KEY")
-JWT_SECRET = os.getenv("JWT_SECRET", "changeme123")
-
-# ===========================================
-# AUTH
-# ===========================================
-security = HTTPBearer()
-
-def verify_jwt(credentials: HTTPAuthorizationCredentials = Depends(security)):
-    token = credentials.credentials
-    if token != JWT_SECRET:
-        raise HTTPException(status_code=403, detail="Invalid token")
-    return True
-
-# ===========================================
-# MODEL LOADER
-# ===========================================
-MODEL_ID = "PuruAI/Medini_Intelligence"
-FALLBACK_MODEL = "gpt2"
-
-def load_llm():
-    pipeline_kwargs = {"max_new_tokens": 512, "temperature": 0.7}
-    try:
-        model_pipeline = pipeline("text-generation", model=MODEL_ID, use_auth_token=HF_TOKEN, **pipeline_kwargs)
-    except Exception:
-        print(f"Warning: Failed to load {MODEL_ID}. Falling back to {FALLBACK_MODEL}.")
-        model_pipeline = pipeline("text-generation", model=FALLBACK_MODEL, **pipeline_kwargs)
-    return HuggingFacePipeline(pipeline=model_pipeline)
-
-llm = load_llm()
-
-# ===========================================
-# VECTOR MEMORY
-# ===========================================
-embeddings = HuggingFaceEmbeddings()
-chroma_db = Chroma(persist_directory="./medini_memory", embedding_function=embeddings)
-retriever = chroma_db.as_retriever()
-
-qa_prompt_template = """
-You are a question-answering system. Use the following context, which contains information retrieved from memory, to answer the user's question. 
-If the context is empty or does not contain the answer, state clearly that the information is not in memory.
-Context:
-{context}
-Question: {question}
-Answer:
-"""
-QA_PROMPT = PromptTemplate(template=qa_prompt_template, input_variables=["context", "question"])
-qa_chain = LLMChain(llm=llm, prompt=QA_PROMPT)
-
-def retrieve_and_answer(question: str) -> str:
-    docs = retriever.get_relevant_documents(question)
-    context = "\n---\n".join([d.page_content for d in docs])
-    return qa_chain.run(context=context, question=question)
-
-# ===========================================
-# TOOLS
-# ===========================================
-search = SerpAPIWrapper(serpapi_api_key=SERPAPI_KEY)
-python_tool = PythonAstREPLTool()  # ✅ Correct REPL
-
-tools = [
-    Tool(
-        name="Knowledge Recall",
-        func=retrieve_and_answer,
-        description="Retrieve info from Medini memory (Chroma DB)."
-    ),
-    Tool(
-        name="Web Search",
-        func=search.run,
-        description="Search the web for up-to-date information."
-    ),
-    Tool(
-        name="Python REPL",
-        func=python_tool.run,
-        description="Execute Python code, useful for math and data manipulation."
-    ),
-]
-
-TOOL_MAP = {tool.name.lower().replace(" ", ""): tool.func for tool in tools}
-
-# ===========================================
-# AGENT
-# ===========================================
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-agent = initialize_agent(
-    tools=tools,
-    llm=llm,
-    agent="conversational-react-description",
-    memory=memory,
-    verbose=True
-)
-
-# ===========================================
-# PLANNER
-# ===========================================
-plan_prompt = PromptTemplate(
-    input_variables=["goal"],
-    template="""
-You are Medini Planner. Decompose the high-level goal into a JSON object containing a 'steps' array (max 6 steps). Each step must have: id, name, description, tool_hint (recall, search, python, agent).
-Return JSON only.
-Goal: {goal}
-"""
-)
-planner_chain = LLMChain(llm=llm, prompt=plan_prompt)
-
-def create_plan(goal: str) -> Dict[str, Any]:
-    raw = planner_chain.run(goal=goal)
-    m = re.search(r"\{.*\}", raw, flags=re.DOTALL)
-    json_str = m.group(0) if m else raw
-    json_str = json_str.replace("```json", "").replace("```", "").strip()
-    try:
-        plan = json.loads(json_str)
-        if 'steps' not in plan:
-            raise ValueError("Parsed JSON is missing 'steps'")
-        return plan
-    except json.JSONDecodeError as e:
-        print(f"JSON Parsing Error: {e} in string: {json_str[:200]}...")
-        raise ValueError("Planner returned malformed JSON.") from e
-
-def execute_step(step: Dict[str, Any]) -> Dict[str, Any]:
-    hint = (step.get("tool_hint") or "").lower()
-    input_text = step.get("description")
-    output, status = "Execution skipped.", "error"
-    try:
-        tool_func = None
-        if "recall" in hint:
-            tool_func = TOOL_MAP.get("knowledgerecall")
-        elif "search" in hint:
-            tool_func = TOOL_MAP.get("websearch")
-        elif "python" in hint:
-            tool_func = TOOL_MAP.get("pythonrepl")
-        if tool_func:
-            output = tool_func(input_text)
-        else:
-            output = agent.run(input_text)
-        status = "ok"
-    except Exception as e:
-        output = f"Execution Error: {str(e)}"
-    chroma_db.add_documents([Document(page_content=f"Step {step['id']} - {step['name']} Result: {output}")])
-    return {"id": step['id'], "name": step['name'], "status": status, "output": output}
-
-def execute_plan(goal: str) -> Dict[str, Any]:
-    try:
-        plan = create_plan(goal)
-    except ValueError as e:
-        return {"goal": goal, "error": str(e)}
-    results = [execute_step(step) for step in plan.get("steps", [])]
-    return {"goal": goal, "plan": plan, "results": results}
-
-# ===========================================
-# FASTAPI BACKEND
-# ===========================================
-app = FastAPI(title="Medini Agent API")
-
-@app.post("/chat")
-def chat_endpoint(message: str, auth: bool = Depends(verify_jwt)):
-    response = agent.run(message)
-    return {"response": response}
-
-@app.post("/goal")
-def goal_endpoint(goal: str, auth: bool = Depends(verify_jwt)):
-    report = execute_plan(goal)
-    return report
-
-# ===========================================
-# GRADIO FRONTEND
-# ===========================================
-def gradio_chat(message, history):
-    try:
-        response = agent.run(message)
-        history.append((message, response))
-    except Exception as e:
-        history.append((message, f"An error occurred: {str(e)}"))
-    return history, ""
-
-def gradio_execute_plan(goal):
-    try:
-        return execute_plan(goal)
-    except Exception as e:
-        return {"error": f"Failed to execute plan: {str(e)}"}
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🤖 Medini Autonomous Agent")
-    gr.Markdown("Chat or submit high-level goals. Agentic AI handles reasoning, memory, and tool use.")
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("## Conversational Chat")
-            chatbot = gr.Chatbot(height=400)
-            msg = gr.Textbox(placeholder="Type your message...", label="Chat Input")
-            clear_btn = gr.Button("Clear Chat")
-            msg.submit(gradio_chat, [msg, chatbot], [chatbot, msg])
-            clear_btn.click(lambda: [], None, chatbot, queue=False)
-
-        with gr.Column(scale=1):
-            gr.Markdown("## Autonomous Goal Planner")
-            goal_input = gr.Textbox(placeholder="Enter high-level goal...", label="Goal")
-            run_goal_btn = gr.Button("Run Goal", variant="primary")
-            gr.Markdown("---")
-            gr.Markdown("### Execution Report")
-            goal_output = gr.JSON(label="Plan and Results")
-            run_goal_btn.click(gradio_execute_plan, [goal_input], goal_output)
-
-# ===========================================
-# LAUNCH
-# ===========================================
-if __name__ == "__main__":
-    def start_api():
-        uvicorn.run(app, host="0.0.0.0", port=8000, log_level="critical")
-    threading.Thread(target=start_api, daemon=True).start()
-    demo.launch(share=False)